Preprints
https://doi.org/10.5194/gmd-2021-333
https://doi.org/10.5194/gmd-2021-333

Submitted as: development and technical paper 19 Oct 2021

Submitted as: development and technical paper | 19 Oct 2021

Review status: this preprint is currently under review for the journal GMD.

Optimization of Snow-Related Parameters in Noah Land Surface Model (v3.4.1) Using Micro-Genetic Algorithm (v1.7a)

Sujeong Lim1,2, Hyeon-Ju Gim3, Ebony Lee1,2,4, Seung-Yeon Lee1,2,4, Won Young Lee1,2, Yong Hee Lee5, Claudio Cassardo6, and Seon Ki Park1,2,4 Sujeong Lim et al.
  • 1Center for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, 03760, Republic of Korea
  • 2Severe Storm Research Center, Ewha Womans University, Seoul, 03760, Republic of Korea
  • 3Korea Institute of Atmospheric Prediction System (KIAPS), Seoul, 07071, Republic of Korea
  • 4Department of Climate and Energy System Engineering, Ewha Womans University, Seoul, 03760, Republic of Korea
  • 5High Impact Weather Research Department, National Institute of Meteorological Sciences, Gangneung, 25457, Republic of Korea
  • 6Department of Physics and NatRisk Centre, University of Torino, Torino, 10125, Italy

Abstract. The snowfall prediction is important in winter and early spring because snowy conditions generate enormous economic damages. However, there is a lack of previous studies dealing with snow prediction, especially using land surface models (LSMs). Numerical weather prediction models directly interpret the snowfall events, whereas the LSMs evaluate the snow cover fraction, snow albedo, and snow depth through interaction with atmospheric conditions. When the initially-developed empirical parameters are local or inadequate, we need to optimize the parameter sets for a certain region. In this study, we seek for the optimal parameter values in the snow-related processes – snow cover fraction, snow albedo, and snow depth – of the Noah LSM, for South Korea, using the micro-genetic algorithm and the in-situ surface observations and remotely-sensed satellite data. Snow data from surface observation stations representing five land cover types – deciduous broadleaf forest, mixed forest, woody savanna, cropland, and urban and built-up lands – are used to optimize five snow-related parameters that calculate the snow cover fraction, maximum snow albedo of fresh snow, and the fresh snow density associated with the snow depth. Another parameter, reflecting the dependence of snow cover fraction on the land cover types, is also optimized. Optimization of these six snow-related parameters has led to improvement in the root-mean squared errors by 17.0 %, 8.2 %, and 5.6 % on snow depth, snow cover fraction, and snow albedo, respectively. In terms of the mean bias, the underestimation problems of snow depth and overestimation problems of snow albedo have been alleviated through optimization of parameters calculating the fresh snow by about 45.1 % and 32.6 %, respectively.

Sujeong Lim et al.

Status: open (until 29 Dec 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2021-333', Juan Antonio Añel, 20 Nov 2021 reply

Sujeong Lim et al.

Sujeong Lim et al.

Viewed

Total article views: 294 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
246 45 3 294 1 0
  • HTML: 246
  • PDF: 45
  • XML: 3
  • Total: 294
  • BibTeX: 1
  • EndNote: 0
Views and downloads (calculated since 19 Oct 2021)
Cumulative views and downloads (calculated since 19 Oct 2021)

Viewed (geographical distribution)

Total article views: 258 (including HTML, PDF, and XML) Thereof 258 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 Nov 2021
Download
Short summary
The land surface model (LSM) contains the various uncertain parameters, which are obtained by the empirical relations reflecting the specific local region and can be a source of uncertainty. To seek the optimal parameter values in the snow-related processes of Noah LSM over South Korea, we have implemented an optimization algorithm, a micro-genetic algorithm using the observations. As a result, the optimized snow parameters improve the snowfall prediction.